Systems and methods for patient-specific dosing

ABSTRACT

This disclosure relates to determining a personalized dose of a pharmaceutical for an individual. First data representative of one or more characteristics of the individual prior to administration of the pharmaceutical is received, and second data representative of a measurement of a physiological parameter of the individual after administration of the pharmaceutical is received. A computational model having pharmacokinetic and pharmacodynamic components is used to generate a first target concentration and one or more first doses determined to likely achieve the first target concentration for the pharmaceutical. The computational model is updated to reflect the measurement of the physiological parameter. A second target concentration and one or more second doses determined to likely achieve the second target concentration are generated, wherein the update to the pharmacodynamic component of the computational model is used to predict that the second target concentration will have a therapeutic effect on the individual.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority to U.S. provisionalapplication Ser. No. 62/145,138, filed Apr. 9, 2015, which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

This disclosure relates generally to patient-specific dosing andtreatment recommendations including, without limitation, computerizedsystems and methods that use medication-specific mathematical models andobserved patient-specific responses to treatment, to predict, propose,and evaluate suitable medication treatment plans for a specific patient.

BACKGROUND

A physician's decision to start a patient on a medication-basedtreatment regimen involves development of a dosing regimen for themedication to be prescribed. Different dosing regimens will beappropriate for different patients having differing patient factors. Byway of example, dosing quantities, dosing intervals, treatment durationand other variables may be varied. Although a proper dosing regimen maybe highly beneficial and therapeutic, an improper dosing regimen may beineffective or deleterious to the patient's health. Further, bothunder-dosing and overdosing generally results in a loss of time, moneyand/or other resources, and increases the risk of undesirable outcomes.

In current clinical practice, the physician typically prescribes adosing regimen based on dosing information contained in the packageinsert (PI) of the prescribed medication. In the United States, thecontents of the PI are regulated by the Food and Drug Administration(FDA). As will be appreciated by those skilled in the art, the PI istypically a printed informational leaflet including a textualdescription of basic information that describes the drug's appearance,and the approved uses of the medicine. Further, the PI typicallydescribes how the drug works in the body and how it is metabolized. ThePI also typically includes statistical details based on trials regardingthe percentage of people who have side effects of various types,interactions with other drugs, contraindications, special warnings, howto handle an overdose, and extra precautions. PIs also include dosinginformation. Such dosing information typically includes informationabout dosages for different conditions or for different populations,like pediatric and adult populations. Typical PIs provide dosinginformation as a function of certain limited patient factor information.Such dosing information is useful as a reference point for physicians inprescribing a dosage for a particular patient.

Dosing information is often developed by the medication's manufacturer,after conducting clinical trials involving administration of the drug toa population of test subjects, carefully monitoring the patients, andrecording of clinical data associated with the clinical trial. Theclinical trial data is subsequently compiled and analyzed to develop thedosing information for inclusion in the PI. The typical dosinginformation is a generic reduction or composite, from data gathered inclinical trials of a population including individuals having variouspatient factors, that is deemed to be suitable for an “average” patienthaving “average” factors and/or a “moderate” level of disease, withoutregard to many of any specific patient's factors, including some patientfactors that may have been collected and tracked during the clinicaltrial. By way of example, based on clinical trial data gathered forAbatacept, an associated PI provides indicated dosing regimens with avery coarse level of detail—such as 3 weight ranges (<60 kg, 60-100 kg,and >100 kg) and associated indicated dosing regimens (500 mg, 750 mg,and 1000 mg, respectively). Such a coarse gradation linked to limitedpatient factors (e.g., weight), ignores many patient-specific factorsthat could impact the optimal or near optimal dosing regimen.Accordingly, it is well-understood that a dosing regimen recommended bya PI is not likely to be optimal or near-optimal for any particularpatient, but rather provides a safe starting point for treatment, and itis left to the physician to refine the dosing regimen for a particularpatient, largely through a trial and error process.

The physician then determines a dosing regimen for the patient as afunction of the PI information. For example, the indicated dosingregimen may be determined to be 750 mg, every 4 weeks, for a patienthaving a weight falling into the 60-100 kg weight range. The physicianthen administers the indicated dosing regimen by prescribing themedication, causing the medication to be administered and/oradministering a dose to the patient consistent with the dosing regimen.

As referenced above, the indicated dosing regimen may be a properstarting point for treating a hypothetical “average” patient, but theindicated dosing regimen is very likely not the optimal or near-optimaldosing regimen for the specific patient being treated. This may be due,for example, to the individual factors of the specific patient beingtreated (e.g., age, concomitant medications, other diseases, renalfunction, etc.) that are not captured by the factors accounted for bythe PI (e.g., weight). Further, this may be due to the coarsestratification of the recommended dosing regimens (e.g., in 40 kgincrements), although the proper dosing is more likely a continuouslyvariable function of one or more patient factors.

Current clinical practice acknowledges this discrepancy. Accordingly, itis common clinical practice to follow-up with a patient after an initialdosing regimen period to reevaluate the patient and dosing regimen.Accordingly, the physician may next evaluate the patient's response tothe indicated dosing regimen. By way of example, this may involveexamining the patient, drawing blood or administering other tests to thepatient and/or asking for patient feedback, such that the patient'sresponse to the previously-administered dosing regimen may be observedby the treating physician. As a result of the evaluation and observedresponse, the physician determines whether a dose adjustment iswarranted, e.g., because the patient response is deficient.

If a dose adjustment is not warranted, then the physician maydiscontinue dosing adjustments. If, however, a dose adjustment iswarranted, then the physician will adjust the dosing regimen ad hoc.Sometimes the suitable adjustment is made solely in the physician'sjudgment. Often, the adjustment is made in accordance with a protocolset forth in the PI or by instructional practice. By way of example, thePI may provide quantitative indications for increasing or decreasing adose, or increasing or decreasing a dosing interval. In either case, theadjustment is made largely on an ad hoc basis, as part of a trial anderror process, and based largely on data gathered after observing theeffect on the patient of the last-administered dosing regimen.

After administering the adjusted dosing regimen, the patient's responseto the adjusted dosing regimen is evaluated. The physician then againdetermines whether to adjust the dosing regimen, and the processrepeats. Such a trial-and-error based approach relying on genericindicated dosing regimens and patient-specific observed responses worksreasonably well for medications with a fast onset of response. However,this approach is not optimal, and often not satisfactory, for drugs thattake longer to manifest a desirable clinical response. Further, aprotracted time to optimize dosing regimen puts the patient at risk forundesirable outcomes.

SUMMARY

Accordingly, systems and methods are disclosed herein for predicting,proposing and/or evaluating suitable medication dosing regimens for aspecific individual as a function of individual-specific characteristicsthat eliminates or reduces the trial and error aspect of conventionaldosing regimen development, and that shortens the length of time todevelop a satisfactory or optimal dosing regimen, and thus eliminates orreduces associated waste of medications, time or other resources andreduces the risk of undesirable outcomes.

One aspect relates to a system for determining a personalized dose of apharmaceutical for an individual. As discussed in detail below, thesystem may be a computer system including a single computer or multiplecomputers communicating over any network, such as in distributedarchitecture. At least one processor may be housed in one, some, or allof the computers in the computer system, and may be in communicationwith at least one electronic database stored on the same computer or ona different computer within the computer system. The system may includea cloud-based set of computing system operated by the same, related, orunrelated entities.

The system includes an input port configured to receive first datarepresentative of one or more characteristics of the individual prior toadministration of the pharmaceutical and second data representative of ameasurement of a physiological parameter of the individual afteradministration of the pharmaceutical. A computer processor is incommunication with the input port and an electronic database havinginformation that represents a computational model to predict an effectof the pharmaceutical on the individual's body. The computational modelincluding a pharmacokinetic component and a pharmacodynamic (e.g.,response to treatment) component, and the computer processor isconfigured to generate, based on the first data and the computationalmodel, a first target concentration and one or more first dosesdetermined to likely achieve the first target concentration for thepharmaceutical in the individual's body. In particular, the one or morefirst doses may be included in one or more dose regimens, where eachdose regimen includes an amount (or dosage) of the pharmaceutical toadminister to the individual, as well as a frequency or time intervalbetween doses. In general, a dose regimen may include a single dose,multiple doses with the same amounts, or multiple doses with differentamounts. Moreover, a dose regimen may include fixed time intervals orvarying time intervals between doses. Then, the computer processorcomputes, based on the second data, an update to the pharmacokineticcomponent and the pharmacodynamic component of the computational modelto obtain an updated computational model that reflects the measurementof the physiological parameter. Based on the updated computationalmodel, a second target concentration and one or more second dosesdetermined to likely achieve the second target concentration for thepharmaceutical in the individual's body are generated. The update to thepharmacodynamic component of the computational model is used to predictthat the second target concentration will have a therapeutic effect onthe individual. To comply with HIPAA requirements, the first data andthe second data may each include an anonymized identifier for theindividual.

In some implementations, the pharmacokinetic component of thecomputational model includes a compartmental model, and the computerprocessor is configured to use the pharmacokinetic component to predicta concentration time profile of the pharmaceutical in at least onecompartment in the compartmental model. The predicted concentration timeprofile is predicted by using a first differential equation thatdescribes a flow rate of the pharmaceutical into and out of the at leastone compartment in the compartmental model. The pharmacodynamiccomponent of the computational model may include a differential equationthat includes a synthesis rate parameter representative of a synthesisrate of a pharmacodynamic marker and a degradation rate parameterrepresentative of a degradation rate of the pharmacodynamic marker and adrug effect component that is reflective of the expected response totherapy of the chosen therapeutic agent. The synthesis rate parameter,the degradation rate parameter, and the drug effect parameters are usedin a second differential equation that predicts the individual'sresponse to the pharmaceutical.

In some implementations, the physiological parameter is a measuredconcentration time profile of the pharmaceutical in the individual'sblood, tissue, or cells, and the computer processor generates the secondtarget concentration and the one or more doses by comparing the measuredconcentration time profile to the predicted concentration time profile.The computational model is then updated to modify the predictedconcentration time profile such that it better matches the measuredconcentration time profile. In particular, this update may includeupdating the pharmacodynamics component of the computational model toassess the patient's individual responsiveness to the therapy and toachieve a particular target concentration. The computer processorgenerates the second target concentration and the one or more seconddoses by performing an optimization technique to minimize a differencebetween the measured concentration time profile and the predictedconcentration. In an illustrative example, the pharmaceutical isinfliximab, and the pharmacodynamic component of the computational modelreflects an effect of infliximab on the individual's body, which isreflected by an altered formation or degradation flow rate based on thedrug effect parameters. The modified flow rate accounts for theindividual's predicted response to the infliximab as the individualheals from his or her disease state. The first target concentration andthe second target concentration may each correspond to a concentrationthat is predicted to cause and maintain an effect in the individual'sbody.

In some implementations, the first target concentration and the one ormore first doses are portions of a first dosing regimen that includesrecommended times and doses to administer to the individual. In thiscase, the input port may be further configured to receive third dataindicative of one or more requirements set by a manufacturer of thepharmaceutical, and the computer processor is further configured tomodify the first dosing regimen to comply with the one or morerequirements while simultaneously using the computational model toreduce an adverse effect of modifying the first dosing regimen.

Another aspect relates to a non-transitory computer readable mediumstoring computer-executable instructions that, when executed by at leastone computer processor, cause a computer system to perform a method fordetermining a personalized dose of a pharmaceutical for an individual.The method includes receiving, at an input port, first datarepresentative of one or more characteristics of the individual prior toadministration of the pharmaceutical. The method also includesgenerating, at a computer processor, based on the first data and acomputational model, a first target concentration and one or more firstdoses determined to likely achieve the first target concentration forthe pharmaceutical in the individual's body, wherein the computerprocessor is in communication with the input port and an electronicdatabase having information that represents the computational model topredict an effect of the pharmaceutical on the individual's body, thecomputational model including a pharmacokinetic component and apharmacodynamic component. Second data is received at the input port,where the second data is representative of a measurement of aphysiological parameter of the individual after administration of thepharmaceutical. The method includes computing, based on the second data,an update to the pharmacokinetic component and the pharmacodynamiccomponent of the computational model to obtain an updated computationalmodel that reflects the measurement of the physiological parameter.Then, based on the updated computational model, a second targetconcentration and one or more second doses determined to likely achievethe second target concentration and achieve a desired response for thepharmaceutical in the individual's body are generated, wherein theupdate to the pharmacodynamic component of the computational model isused to predict that the second target concentration will have atherapeutic effect on the individual.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features of the present disclosure, including itsnature and its various advantages, will be more apparent uponconsideration of the following detailed description, taken inconjunction with the accompanying drawings in which:

FIG. 1 is a block diagram of a computerized system for usingmedication-specific mathematical models and observed patient-specificresponses to treatment to predict, propose, and evaluate suitablemedication treatment plans for a specific patient, according to anillustrative implementation.

FIG. 2 is a block diagram of a pharmacokinetic/pharmacodynamic modelthat can be used to determine a target level of a physiologicalparameter for a specific patient and provide a suggested dosing regimen,according to an illustrative implementation.

FIG. 3 is a flowchart of a method used by a computerized system toprovide a recommended dosing regimen for a specific patient, accordingto an illustrative implementation.

FIGS. 4A and 4B are example displays of a user interface on a clinicalportal that provide graphs of predicted concentration time profiles,according to an illustrative implementation.

FIGS. 5A and 5B are example displays of a user interface on a clinicalportal that provide several recommended dosing regimens, according to anillustrative implementation.

FIG. 6 is a block diagram of a computing device for performing any ofthe processes described herein, according to an illustrativeimplementation.

DETAILED DESCRIPTION

Described herein are medical treatment analysis and recommendationsystems and methods that provide a tailored approach to analyzingpatient measurements and to generating recommendations that areresponsive to a patient's specific response to a treatment plan. Toprovide an overall understanding, certain illustrative implementationswill now be described, including a system for predicting a patient'sresponse to a treatment plan and providing a patient-specific dosingregimen. However, it will be understood by one of ordinary skill in theart that the systems and methods described herein may be adapted andmodified as is appropriate for the application being addressed and maybe employed in other suitable applications, and that such otheradditions and modifications will not depart from the scope thereof.

The present disclosure provides systems and methods for providingpatient-specific medication dosing as a function of mathematical modelsupdated to account for an observed patient response, such as a bloodconcentration level, or a measurement such as blood pressure orhematocrit. In particular, the systems and methods described hereininvolve predicting, proposing and/or evaluating suitable medicationdosing regimens for a specific individual as a function ofindividual-specific characteristics and observed responses of thespecific individual to the medication. Conceptually, the prescribingphysician is provided with access, in a direct way, to mathematicalmodels of observed patient responses to a medication when prescribingthe medication to a specific patient. In prescribing a treatment planfor a patient, the mathematical model is used to predict a specificpatient's response as a function of patient-specific characteristicsthat are accounted for in the model as patient factor covariates.Accordingly, the prescribing physician is able to leverage the model indeveloping a reasonably tailored treatment plan for a specific patient,as a function of the specific patient's characteristics, with muchgreater precision than a PI can provide.

Bayesian analysis may be used to determine a recommended dosing regimen.This is described in detail in U.S. patent application Ser. No.14/047,545, filed Oct. 7, 2013 and entitled “System and method forproviding patient-specific dosing as a function of mathematical modelsupdated to account for an observed patient response” (“the '545application”), which is incorporated herein by reference in itsentirety. As is described in the '545 application, a Bayesian analysismay be used to determine an appropriate dose needed to achieve adesirable result, such as maintaining a drug's concentration in thepatient's blood near a particular level. In particular, the Bayesiananalysis may involve Bayesian averaging, Bayesian forecasting, andBayesian updating.

Importantly, not only do the systems and methods of the presentdisclosure provide a recommendation for a dosing regimen to achieve aparticular target level for a physiological parameter in a specificpatient (such as the concentration level of a drug or biomarker in thepatient's blood, for example), but the present disclosure also providesa way to determine whether that particular target level would beeffective for the specific patient. In one example, as is shown anddescribed in relation to FIG. 2 , the mathematical model includes apharmacokinetic (PK) model (that predicts the time course of thepresence of a drug in a body) and a pharmacodynamic (PD) model (thatpredicts the resulting therapeutic and/or adverse effects of a drug in abody) combined together. As is explained in relation to FIG. 2 , theresulting pharmacokinetic/pharmacodynamic (PK/PD) model provides arecommendation for a specific target level that is predicted to resultin a therapeutic response for a particular patient.

In addition, the specific patient's observed response to the initialdosing regimen is used to adjust the dosing regimen. Specifically, thepatient's observed response is used in conjunction with the mathematicalmodel and patient-specific characteristics to account forbetween-subject-variability (BSV) that cannot be accounted for by themathematical model alone. Accordingly, the observed responses of thespecific patient can be used to refine the models and related forecasts,to effectively personalize the models so that they may be used toforecast expected responses to proposed dosing regimens more accuratelyfor a specific patient. In this manner, observed patient-specificresponse data is effectively used as “feedback” to adapt a generic modeldescribing typical patient response to a patient-specific model capableof accurately forecasting a patient-specific response, such that apatient-specific dosing regimen can be predicted, proposed and/orevaluated on a patient-specific basis. Using the observed response datato personalize the models allows the models to be modified to accountfor BSV that is not accounted for in previous mathematical models, whichdescribed only typical responses for a patient population, or a “typicalfor covariates” response for a typical patient having certaincharacteristics accounted for as covariates in the model.

The systems and methods of the present disclosure allows the prescribingphysician to develop a personalized dosing regimen using one or moremathematical models reflecting actual clinical data, without the loss ofresolution in the data and/or model that results from distillation ofthe actual clinical data into a relatively coarsely stratified set ofrecommendations for an “average” or “typical” patient, as in a Pl. Themodel-based development of such patient-specific medication dosingregimens eliminates or reduces the trial-and-error aspect ofconventional dosing regimen development. Further, such model-baseddevelopment shortens the length of time to develop a satisfactory oroptimal dosing regimen, and thus eliminates or reduces associated wasteof medications, time or other resources, as well as reduces the amountof time that a patient is at risk of undesirable outcomes.

Generally, mathematical models developed from clinical data are gatheredfrom patients to whom a particular medication had been administered.These models are processed to create a composite model rich in patientdata, and patient-specific dosing regimens are determined as a functionof patient-specific observed response data processed in conjunction withdata from the mathematical models. More specifically, as is described inthe '545 application, Bayesian averaging, Bayesian updating, andBayesian forecasting techniques may be used to develop patient-specificdosing regimens as a function of not only generic mathematical modelsand patient-specific characteristics accounted for in the models ascovariate patient factors, but also observed patient-specific responsesthat are not accounted for within the models themselves, and thatreflect BSV that distinguishes the specific patient from the typicalpatient reflected by the model.

In this manner, the present disclosure accounts for variability betweenindividual patients that is unexplained and/or unaccounted for bytraditional mathematical models (e.g., patient response that would nothave been predicted based solely on the dose regimen and patientfactors). Further, the present disclosure allows patient factorsaccounted for by the models, such as weight, age, race, laboratory testresults, etc., to be treated as continuous functions rather than ascategorical (cut off) values. By doing this, known models are adapted toa specific patient, such that patient-specific forecasting and analysiscan be performed, to predict, propose and/or evaluate dosing regimensthat are personalized for a specific patient. Notably, the presentdisclosure may be used to not only retroactively assess a dosing regimenpreviously administered to the patient, but also to prospectively assessa proposed dosing regimen before administering the proposed dosingregimen to the patient, or to identify dosing regimens (administereddose, dose interval, and route of administration) for the patient thatwill achieve the desired outcome.

By refining a particular patient's initial dosing regimen as a functionof observed patient-specific data, in view of the composite mathematicalmodel, a personalized, patient-specific dosing regimen is developed, andfurther is developed quickly. It will be appreciated that the exemplarymethod is implemented and carried out by a computerized model-basedpatient specific medication dosing regimen recommendation system 100with input provided by a human operator, such as a physician or othermedical professional, and thus acts as a recommendation engine and/orphysician's expert system providing information for consideration by aprescribing physician.

FIG. 1 is a block diagram of a computerized system 100 for implementingthe systems and methods disclosed herein. In particular, the system 100uses medication-specific mathematical models and observedpatient-specific responses to treatment to predict, propose, andevaluate suitable medication treatment plans for a specific patient. Thesystem 100 includes a server 104, a clinical portal 114, a pharmacyportal 124, and an electronic database 106, all connected over a network102. The server 104 includes a processor 105, the clinical portal 114includes a processor 110 and a user interface 112, and the pharmacyportal 124 includes a processor 120 and a user interface 122. As usedherein, the term “processor” or “computing device” refers to one or morecomputers, microprocessors, logic devices, servers, or other devicesconfigured with hardware, firmware, and software to carry out one ormore of the computerized techniques described herein. Processors andprocessing devices may also include one or more memory devices forstoring inputs, outputs, and data that is currently being processed. Anillustrative computing device 600, which may be used to implement any ofthe processors and servers described herein, is described in detailbelow with reference to FIG. 6 . As used herein, “user interface”includes, without limitation, any suitable combination of one or moreinput devices (e.g., keypads, touch screens, trackballs, voicerecognition systems, etc.) and/or one or more output devices (e.g.,visual displays, speakers, tactile displays, printing devices, etc.). Asused herein, “portal” includes, without limitation, any suitablecombination of one or more devices configured with hardware, firmware,and software to carry out one or more of the computerized techniquesdescribed herein. Examples of user devices that may implemental a portalinclude, without limitation, personal computers, laptops, and mobiledevices (such as smartphones, blackberries, PDAs, tablet computers,etc.). For example, a portal may be implemented over a web browser or amobile application installed on the user device. Only one server, oneclinical portal 114, and one pharmacy portal 124 are shown in FIG. 1 toavoid complicating the drawing; the system 100 can support multipleservers and multiple clinical portals and pharmacy portals.

In FIG. 1 , a patient 116 is examined by a medical professional 118, whohas access to the clinical portal 114. The patient may be subject to adisease that has a known progression, and consults the medicalprofessional 118. The medical professional 118 makes measurements fromthe patient 116 and records these measurements over the clinical portal114. For example, the medical professional 118 may draw a sample of theblood of the patient 116, and may measure a concentration of a biomarkerin the blood sample.

In general, the medical professional 118 may make any suitablemeasurement of the patient 116, including lab results such asconcentration measurements from the patient's blood, urine, saliva, orany other liquid sampled from the patient. The measurement maycorrespond to observations made by the medical professional 118 of thepatient 116, including any symptoms exhibited by the patient 116. Forexample, the medical professional 118 may perform an examination of thepatient gather or measure patient-specific factors such as sex, age,weight, race, disease stage, disease status, prior therapy, otherconcomitant diseases and/or other demographic and/or laboratory testresult information. More specifically, this involves identifying patientcharacteristics that are reflected as patient factor covariates withinthe mathematical model that will be used to predict the patient'sresponse to a drug treatment plan. For example, if the model isconstructed such that it describes a typical patient response as afunction of weight and gender covariates, the patient's weight andgender characteristics would be identified. Any other characteristicsmay be identified that are shown to be predictive of response, and thusreflected as patient factor covariates, in the mathematical models. Byway of example, such patient factor covariates may include weight,gender, race, lab results, disease stage and other objective andsubjective information.

Based on the patient's measurement data, the medical professional 118may make an assessment of the patient's disease status, and may identifya drug suitable for administering to the patient 116 to treat thepatient 116. The clinical portal 114 may then transmit the patient'smeasurements, the patient's disease status (as determined by the medicalprofessional 118), and an identifier of the drug over the network 102 tothe server 104, which uses the received data to select one or moreappropriate computational models from the models database 106. Theappropriate computational models are those that are determined to becapable of predicting the patient's response to the administration ofthe drug. The one or more selected computational models are used todetermine a recommended set of planned dosages of the drug to administerto the patient, and the recommendation is transmitted back over thenetwork 102 to the clinical portal 114 for viewing by the medicalprofessional 118.

Alternatively, the medical professional 118 may not be capable ofassessing the patient's disease status or identify a drug, and either orboth of these steps may be performed by the server 104. In this case,the server 104 receives the patient's measurement data, and correlatesthe patient's measurement data with the data of other patients in thepatient database 106 a. The server 104 may then identify other patientswho exhibited similar symptoms or data as the patient 116 and determinethe disease states, drugs used, and outcomes for the other patients.Based on the data from the other patients, the server 104 may identifythe most common disease states and/or drugs used that resulted in themost favorable outcomes, and provide these results to the clinicalportal 114 for the medical professional 118 to consider.

As is shown in FIG. 1 , the database 106 includes a set of fourdatabases including a patient database 106 a, a disease database 106 b,a treatment plan database 106 c, and a models database 106 d. Thesedatabases store respective data regarding patients and their data,diseases, drugs, dosage schedules, and computational models. Inparticular, the patient database 106 a stores measurements taken by orsymptoms observed by the medical professional 118. The disease database106 b stores data regarding various diseases and possible symptoms oftenexhibited by patients infected with a disease. The treatment plandatabase 106 c stores data regarding possible treatment plans, includingdrugs and dosage schedules for a set of patients. The set of patientsmay include a population with different characteristics, such as weight,height, age, sex, and race, for example. The models database 106 dstores data regarding a set of computational models that may be used todescribe PK, PD, or both PK and PD changes to a body. One example of aPK/PD model is described in relation to FIG. 2 and EQS. 1-16.

Any suitable mathematical model may be stored in the models database 106d, such as in the form of a compiled library module, for example. Inparticular, a suitable mathematical model is a mathematical function (orset of functions) that describes the relationship between a dosingregimen and the observed patient exposure and/or observed patientresponse (collectively “response”) for a specific medication.Accordingly, the mathematical model describes response profiles for apopulation of patients. Generally, development of a mathematical modelinvolves developing a mathematical function or equation that defines acurve that best “fits” or describes the observed clinical data, as willbe appreciated by those skilled in the art.

Typical models also describe the expected impact of specific patientcharacteristics on response, as well as quantify the amount ofunexplained variability that cannot be accounted for solely by patientcharacteristics. In such models, patient characteristics are reflectedas patient factor covariates within the mathematical model. Thus, themathematical model is typically a mathematical function that describesunderlying clinical data and the associated variability seen in thepatient population. These mathematical functions include terms thatdescribe the variation of an individual patient from the “average” ortypical patient, allowing the model to describe or predict a variety ofoutcomes for a given dose and making the model not only a mathematicalfunction, but also a statistical function, though the models andfunctions are referred to herein in a generic and non-limiting fashionas “mathematical” models and functions.

It will be appreciated that many suitable mathematical models alreadyexist and are used for purposes such as drug product development.Examples of suitable mathematical models describing response profilesfor a population of patients and accounting for patient factorcovariates include PK models, PD models, hybrid PK/PD models, andexposure/response models. Such mathematical models are typicallypublished or otherwise obtainable from medication manufacturers, thepeer-reviewed literature, and the FDA or other regulatory agencies.Alternatively, suitable mathematical models may be prepared by originalresearch. Moreover, as is described in the '545 application, a Bayesianmodel averaging approach may be used to generate a composite model topredict patient response when multiple patient response models areavailable, though a single model may also be used.

In particular, the output of the PK/PD model corresponds to a dosingregimen or schedule that achieves an optimal target level for aphysiological parameter of the patient 116. The PK/PD model provides theoptimal target level as a recommendation specifically designed for thepatient 116, and has verified that the optimal target level is expectedto produce an effective and therapeutic response in the patient 116. Inthe example shown and described in relation to FIG. 2 , thephysiological parameter corresponds to a concentration of a drug in thepatient's blood, though in general, the physiological parameter maycorrespond to any number of measurements from a patient. When the drugis infliximab, for example, it may be desirable to measure the drugconcentration (and predict the drug concentration using a PK model, asis described in detail below) and other measurable units (that may bepredicted by a PD model, for example), such as C reactive protein,endoscopic disease severity, and fecal calprotectin. Each measurable(e.g., the drug concentration, C reactive protein, endoscopic diseaseseverity, and fecal calprotectin) may involve one or more PK and/or PDmodels. The interaction between PK and PD models may be particularlyimportant for a drug like infliximab, in which patients with more severedisease clear the drug faster (modeled by higher clearance from a PKmodel, as is explained in detail below). One goal of the drug infliximabmay be to normalize C reactive protein levels, lower fecal calprotectinlevels, and achieve endoscopic remission.

In one example, the medical professional 118 may assess the likelihoodthat the patient 116 will exhibit a therapeutic response to a particulardrug and dosing regimen. In particular, this likelihood may be low ifseveral dosing regimens of the same drug have been administered to thepatient, but no measurable response from the patient is detected. Inthis case, the medical professional 118 may determined that it isunlikely that the patient will response to further adjustments to thedose, and other drugs may be considered. Moreover, as is described indetail below, a confidence interval may be assessed for the predictedmodel results (e.g., the predicted exposure to the drug (as provided bythe PK model) and the predicted response of the body to the presence ofthe drug (as provided by the PD model). As data is collected from thepatient 116, the confidence interval gets narrower, and is indicative ofa more trustworthy result and recommendation.

Importantly, the systems and methods of the present disclosure allow forthe simultaneous interaction and fit of the PK and PD models. The PDmodel is used to identify an individualized target level, and the PKmodel is used to provide individualized dosing recommendations based onthe individualized target level. Moreover, by simultaneously fittingboth a PK model and a PD model, the two models that predict drug leveland therapeutic response are allowed to interact in a manner that ismore physiologically realistic than other models.

Often, the medical professional 118 may be a member or employee of amedical center. The same patient 116 may meet with multiple members ofthe same medical center in various roles. In this case, the clinicalportal 114 may be configured to operate on multiple user devices. Themedical center may have its own records for the particular patient. Insome implementations, the present disclosure provides an interfacebetween the computational models described herein and a medical center'srecords. For example, any medical professional 118, such as a doctor ora nurse, may be required to enter authentication information (such as ausername and password) or scan an employee badge over the user interface112 to log into the system provided by the clinical portal 114. Oncelogged in, each medical professional 118 may have a corresponding set ofpatient records that the professional is allowed to access.

In some implementations, the patient 116 interacts with the clinicalportal 114, which may have a patient-specific page or area forinteraction with the patient 116. For example, the clinical portal 114may be configured to monitor the patient's treatment schedule and sendappointments and reminders to the patient 116. Moreover, one or moredevices (such as smart mobile devices or sensors) may be used to monitorthe patient's ongoing physiological data, and report the physiologicaldata to the clinical portal 114 or directly to the server 104 over thenetwork 102. The physiological data is then compared to expectations,and deviations from expectations are flagged. Monitoring the patient'sdata on a continual basis in this manner allows for possible earlydetection of deviations from expectations of the patient's response to adrug, and may indicate the need for early intervention or alternatetherapy.

As described herein, the measurements from the patient 116 that areprovided into the computational model may be determined from the medicalprofessional 118, directly from devices monitoring the patient 116, or acombination of both. Because the computational model predicts a timeprogression of the disease and the drug, and their effects on the body,these measurements may be used to update the model parameters, so thatthe treatment plan (that is provided by the model) is refined andcorrected to account for the patient's specific data.

In some implementations, it is desirable to separate a patient'spersonal information from the patient's measurement data that is neededto run the computational model. In particular, the patient's personalinformation may be protected health information (PHI), and access to aperson's PHI should be limited to authorized users. One way to protect apatient's PHI is to assign each patient to an anonymized code when thepatient is registered with the server 104. The code may be manuallyentered by the medical professional 118 over the clinical portal 114, ormay be entered using an automated but secure process. The server 104 maybe only capable of identifying each patient according to the anonymizedcode, and may not have access to the patient's PHI. In particular theclinical portal 114 and the server 104 may exchange data regarding thepatient 116 without identifying the patient 116 or revealing thepatient's PHI.

The generation or selection of the code may be performed in a similarmanner as is done for credit card systems. For example, all access tothe system may be protected by an application programming interface(API) key. Moreover, when the medical professional 118 is part of amedical center, the medical center's connection to the network 102 overthe clinical portal 114 may have enhanced security systems in compliancewith HIPAA. As an example, a single administrative database may defineaccess in a manner that ensures that members of one team (e.g., one setof medical professionals, for example) are prohibited from viewingrecords associated with another team. To implement this, each end-userapplication may be issued a single API key that specifies which portionsof a database may be accessed.

In some implementations, multiple levels of clinician interaction withthe portal are configured. For example, some medical professionals, uponlogging into the clinical portal 114, may have access that only allowsthem to view the patient's data. Another level of access may allow themedical professional 118 to view the patient's data as well as entermeasurement and observation data regarding the patient 116. A thirdlevel of access may allow the medical professional 118 to view andupdate the patient's data, as well as prescribe a treatment for thepatient 116 or otherwise update the patient's treatment plan or dosingschedule.

Different levels of access may be set for different types of users. Forexample, a user who is a system administrator for the clinical portal114 may be able to grant or rescind access to the system to other users,but does not have access to any patient records. As another example, aprescriber may be allowed to modify a particular patient's treatmentplan and has read and write access to patient records. A reviewer mayhave just read-only access to patient records, and can only view apatient's treatment plan. A data manager may have read and write accessto the patient records, but may not be allowed to modify a patient'streatment plan.

In some implementations, the clinical portal 114 is configured tocommunicate with the pharmacy portal 124 over the network 102. Inparticular, after a dosing regimen is selected to be administered to thepatient 116, the medical professional 118 may provide an indication ofthe selected dosing regimen to the clinical portal 114 for transmittingthe selected dosing regimen to the pharmacy portal 124. Upon receivingthe dosing regimen, the pharmacy portal 124 may display the dosingregimen and an identifier of the medical professional 118 over the userinterface 122, which interacts with the pharmacist 128 to fulfill theorder.

In some implementations, recommendations or custom orders for drugamounts is provided to drug manufacturers (not shown), who may haveaccess to the network 102. Manufacturers of drugs may only producecertain drugs at set amounts or volumes, which may correspond torecommended dosage amounts for the “typical” patient. This may beespecially true for expensive drugs. However, as is described herein,the optimal amount or dosing schedule of a drug for a specific patientmay be different for different patients. Moreover, some drugs haveexpiration dates or have decreased efficacy over time as the drug sitson the shelf. Thus, if it is desirable to administer the optimal amountof drug according to a recommended dosing regimen, then this couldpotentially lead to drug wastage at least because the optimal amount maynot correspond to an integer multiple of the set amount that is producedby the manufacturer.

One way for this problem to be mediated is to provide information to thedrug manufacturer reflective of the recommended dosing regimen ahead oftime, so that the drug manufacturer can produce custom sized orders forcertain medications at the desired times according to the regimen. Inthis manner, the present disclosure allows for drugs to be freshlyproduced in the desired amounts at a time that is as close to theadministration time as possible.

Moreover, clinical phase IV drug trials are often limited due to theexpensive cost of the drugs. The present disclosure provides a way fordata regarding a subject's specific response to a drug to be fed backinto the models to adequately capture the subject's specific data. Thepresent disclosure provides an automated method of computing arecommended dosage schedule that is deterministic. The dosage schedulecan be supplied economically and quickly in a secure manner (e.g.,without revealing the patient's PHI) to the drug manufacturer, who maythen manufacture customized orders, thereby saving on cost and leadingto reduced drug wastage. Moreover, the manufacturer of the drug may beinterested in the tested efficacy of the drug, and may be able to adjustthe amounts of the drug that are produced and/or the production timelineto accommodate various dosing regimens.

In addition, to the extent that a drug manufacturer's timeline islimited by certain factors, the present disclosure is capable ofproviding recommended dosing regimens within the limits of the drugmanufacturer. For example, for technological and/or economical reasons,the drug manufacturer may only be able to produce a drug in setquantities. Because a dosing regimen often involves two parameters(namely, an amount of a drug and a time at which to administer thedrug), the recommended dosing regimen provided by the system 100 may bemodified accordingly to accommodate the drug manufacturer's limits.

As is shown in FIG. 1 , the server 104 is a device (or set of devices)that is remote from the clinical portal 114. Depending on thecomputational power of the device that houses the clinical portal 114,the clinical portal 114 may simply be an interface that primarilytransfers data between the medical professional 118 and the server 104.Alternatively, the clinical portal 114 may be configured to locallyperform any or all of the steps described to be performed by the server104, including but not limited to receiving patient symptom andmeasurement data, accessing any of the databases 106, running one ormore computational models, and providing a recommendation for a dosageschedule based on the patient's specific symptom and measurement data.Moreover, while FIG. 1 depicts the patient database 106 a, the diseasedatabase 106 b, the treatment plan database 106 c, and the modelsdatabase 106 d as being entities that are separate from the server 104,the clinical portal 114, or the pharmacy portal 124, one of ordinaryskill in the art will understand that any or all of the databases 106may be stored locally on any of the devices or portals described herein,without department from the scope of the present disclosure.

FIG. 2 is a block diagram 200 of an illustrative compartmental model forpharmacokinetics and pharmacodynamics. A compartmental model generallydescribes the result when a drug enters a body, which is represented asone or more compartments, which may represent one or more organs ortissues within the body. Specifically, the drug enters the body via asite of administration and enters a central compartment. From thecentral compartment, the drug may be exchanged with one or moreperipheral compartments that represent distribution of the drug to otherregions of the body. The drug may also be eliminated from the centralcompartment via metabolism or excretion processes. The movement of thedrug (into and out of the central compartment and any peripheralcompartments) may be represented by using transfer rate constants

For example, a PK model 220 for infliximab (IFX) may include the twocompartments shown in FIG. 2 , which includes a central compartment 204and a peripheral compartment 206. The central compartment 204 maygenerally represent blood circulation in an organism and corresponds toa relatively rapid distribution. For example, the central compartmentmay represent organs and systems within an organism that have awell-developed blood supply, such as the liver or kidney. In contrast,the peripheral compartment 206 may represent organs or systems that havelower blood flow, such as muscle, lean tissue, and fat.

In addition to the two compartments in the PK model 220, FIG. 2 alsodepicts input flows and output flows into and out of the compartments.In particular, the IV infusion 202 corresponds to a flow rate ofentrance of the drug into the body via the site of administration andinto the central compartment 204. The clearance (CL) 210 corresponds toan exit flow rate out of the central compartment 204, and may berepresentative of an amount of drug that is flushed out of the system,such as via metabolism or excretion processes. The inter-compartmentalclearance (Q) 208 corresponds to a flow rate between the centralcompartment 204 and the peripheral compartment 206, and representsdistribution of the drug between organs with higher blood flow andorgans with lower blood flow.

For IFX, the following system of equations may be used to represent thePK model.

$\begin{matrix}{\left. {{CL} = {\left( {\theta_{1}*\left( \frac{{Weig}ht}{70} \right.} \right)^{\theta_{6}}*\left( \frac{ALB}{4} \right)^{\theta_{10}}*\left( \frac{AST}{30} \right)^{\theta_{11}}*\left( {1 + {\theta_{12}*{IRP}}} \right)}} \right)*\exp\left( \eta_{1} \right)} & {{EQ}.1}\end{matrix}$ $\begin{matrix}{{V1} = {\theta_{2}*\left( \frac{Weight}{70} \right)^{\theta_{7}}*\exp\left( \eta_{2} \right)}} & {{EQ}.2}\end{matrix}$ $\begin{matrix}{Q = {\left( {\theta_{3}*\left( \frac{Weight}{70} \right)^{\theta_{8}}*\left( \frac{ALB}{4} \right)^{\theta_{13}}} \right)*\exp\left( \eta_{3} \right)}} & {{EQ}.3}\end{matrix}$ $\begin{matrix}{{V2} = {\theta_{4}*\left( \frac{Weight}{70} \right)^{\theta_{9}}*\exp\left( \eta_{4} \right)}} & {{EQ}.4}\end{matrix}$

In the above set of equations, the set of values for θ_(i) denotes avector of fixed-effect parameters that represent structural parametersof the model. The parameter “weight” represents the weight of thepatient in kilograms, ALB represents a level of albumin in grams perdeciliter, AST represents a level of aspartate aminotransferase ininternational units per liter, and IRP represents Immune ResponsePositive. These parameters are examples of physiological parameters thatare measurable directly from the patient. As used in EQ. 1, the valuefor IRP is indicative of whether the patient has developed antibodiesagainst the drug IFX. If so, this increases the clearance of the drugout of the central compartment 204. The parameter V1 corresponds to thevolume of distribution of the central compartment 204, and the parameterV2 corresponds to the volume of distribution of the peripheralcompartment 206. The set of values for η_(i) represents between-subjectvariability for the clearance (η₁), the volume V1 (η₂), theinter-compartmental clearance Q (η₃), and the volume V2 (η₄). Generally,the values m represent the unexplained random variability that is notcaptured by patient factors.

EQS. 1-4 represent a two-compartmental PK model for the distribution ofIFX in a body, and the patient-specific parameters that are not readilymeasurable are solved for based on measurable patient parameters. Inparticular, none of the parameters in the above set of equations may bereadily measurable from a patient, but these parameters may be inferredfrom measurements of concentration of a drug in a patient's blood.

For example, the concentration time profile of IFX in a patient's bloodmay be measured and then compared to a predicted time profile of IFXusing the following set of equations. The parameters above, includingCL, V1, Q, V2, θ_(i), and η_(i), may then be fit to result in apredicted concentration time profile that resembles the measuredconcentration time profile.

$\begin{matrix}{\frac{{dA}(1)}{dt} = {{{- \left( \frac{CL}{V1} \right)}*{A(1)}} - {\left( \frac{Q}{V1} \right)*{A(1)}} + {\left( \frac{Q}{V2} \right)*{A(2)}}}} & {{EQ}.5}\end{matrix}$ $\begin{matrix}{\frac{{dA}(2)}{dt} = {{\left( \frac{Q}{V1} \right)*{A(1)}} - {\left( \frac{Q}{V2} \right)*{A(2)}}}} & {{EQ}.6}\end{matrix}$

In EQS. 5 and 6 above, A(1) denotes an amount of IFX in the centralcompartment 204, and A(2) denotes an amount of IFX in the peripheralcompartment 206. In particular, EQ. 5 represents the net flow rate ofthe drug IFX into the central compartment 204, after accounting for theclearance 210, and the inter-compartmental clearance 208. As is shown inEQ. 5, the flow rate of the IV infusion 202 is not included. However, itwill be understood that EQ. 5 may be modified to include the flow rateof input of the drug during the time(s) of infusion. For example, theright hand side of EQ. 5 may be modified to include a flow rateparameter R0, which is set to the input flow rate of the drug duringinfusion time, and zero when no infusion takes place. The

$\left( \frac{CL}{V1} \right)*{A(1)}{and}\left( \frac{Q}{V1} \right)*{A(1)}$

terms in EQ. 5 are negative because these correspond to flow rates ofIFX exiting the central compartment 204, while the term

$\left( \frac{Q}{V2} \right)*{A(2)}$

corresponds to an input flow rate of IFX from the peripheral compartment206. Similarly, EQ. 6 represents the net flow rate of IFX into theperipheral compartment 206, after accounting for the inter-compartmentalclearance 208 that enters the peripheral compartment 206 (correspondingto the positive term

$\left. {\left( \frac{Q}{V1} \right)*{A(1)}} \right)$

and that exits the peripheral compartment 206 (corresponding to thenegative term

$\left. {\left( \frac{Q}{V2} \right)*{A(2)}} \right).$

The initial conditions may be set such that both A(1) and A(2) areinitially zero (before administration of any IFX). As discussed above,EQS. 1-6 may be used to predict a concentration time profile of IFX inthe central compartment 204. The concentration time profile may berepresented as

$\frac{{A(1)}(t)}{V1},$

or the amount of IFX in the central compartment 204 as a function oftime, divided by the volume of the central compartment 204. The profile

$\frac{{A(1)}(t)}{V1}$

may be referred to herein as a predicted concentration time profile,because the profile results from model predictions, and not from directmeasurements.

To determine whether the model predictions and values for the modelparameters are reasonable, the predicted concentration time profile iscompared to a measured concentration time profile. The measuredconcentration time profile may be directed measured by sampling apatient's blood at different times, and measuring the concentration ofIFX in the blood. An optimization technique may be performed to computeestimates for CL, V1, Q, and V2 by determining values for the set oftheta values θ_(i), and estimating difference parameter values for theset of eta values (which represent unexplained variability), thatminimize the error between the measured concentration time profile andthe predicted concentration time profile.

$\begin{matrix}{{{Concentration}(t)} = {\frac{{A(1)}(t)}{V1}*\exp\left( \varepsilon_{1} \right)}} & {{EQ}.7}\end{matrix}$

In EQ. 7, the parameter concentration(t) corresponds to the measuredconcentration of IFX in a patient's blood as a function of time, whilethe values for A(1)(t) and V1 are provided from the two-compartmental PKmodel. The parameter ε₁ corresponds to a residual error that isrepresentative of measurement error. The optimization may be performedto minimize the residual error between the measurements and predictions.As is shown in FIG. 2 , the predicted concentration 212 is provided bythe PK model 220 to a PD model 222.

In contrast to the above-described PK model 220, a PD model 222 predictsthe physiological and biochemical effects of a drug on a body. Inparticular, the effect of a drug and the drug's concentration may berepresented with a sigmoidal curve. In this case, for drugconcentrations below a first threshold, the effect of the drug may beminimal and may have little to no effect. For drug concentrations abovea second threshold (higher than the first threshold), the effect of thedrug may be maximal, and higher concentration would not result in muchincreased effect. This maximal drug effect is represented by a unitlessparameter Emax, which may be defined as in EQ. 8.

Emax=θ₁₄*exP(η₅)  EQ. 8

Moreover, the concentration of a drug that is between the first andsecond thresholds, and that produces an effect of the drug at half ofthe maximal effect Emax, is referred to as EC50 (having units ofamount/volume), and is described in EQ. 9.

EC50=θ₁₅*exp(η₆)  EQ. 9

The concentration that achieves half the maximal response (EC50)represents a key parameter in the PD model and may be used to determineappropriate drug exposure to maintain therapeutic response. Importantly,the particular value for EC50 for different patients may be different,as is denoted with the between subject variability parameter η₆. Thisindicates that the target concentration necessary to achieve maximalmeaningful clinical effect for different patients may be different, andshould therefore be estimated individually for each patient. In someimplementations, the target concentration does not correspond to theconcentration that achieves half the maximal response. In particular,higher or lower values may be used, and are dependent on the particulargoal to be achieved. For example, if a drug lowers blood pressure, thismay mean that the maximal response of the body to the drug means thatthe blood pressure is reduced to zero. In this case, a drugconcentration that achieves half of this maximal response may be toosevere, and a much lower target value for the drug concentration may beused instead. However, even in this case, knowledge of the maximalresponse and what the value of the concentration is that achieves halfthe maximal response may be important in determining the targetconcentration. By estimating the EC50 parameter on a patient-by-patientbasis, the systems and methods of the present disclosure use acomputational method of determining the relevant target concentration ofa drug in an individual that is likely to produce a therapeuticresponse.

In a turnover PD model, parameters are used to represent a base amountof a PD marker prior to drug administration (e.g., Base) and a synthesisrate of the PD marker (e.g., Ksyn 214, having units amount/time).

Base=θ₁₆*exp(η₇)  EQ. 10

Ksyn=θ₁₇*exp(η₈)  EQ. 11

Base corresponds to the PD response prior to the first administration ofdrug, and Ksyn 214 is the rate of formation of the PD marker. Moreover,in the untreated state, the parameters Base and Ksyn are related to eachother according to EQ. 12, which describes a ratio of Ksyn and Base as adegradation rate of the PD marker (e.g., Kdeg 218, having units 1/time),since the undisturbed baseline value is the ratio of formation anddegradation of the PD marker.

$\begin{matrix}{{Kdeg} = \frac{Ksyn}{Base}} & {{EQ}.12}\end{matrix}$

In particular, the parameters described in relation to EQS. 8-12 areused to model C reactive protein, which is one of the markers of diseaseactivity that may be tracked when IFX is used. In general, the sameparameters in EQS. 8-12 may be used to model C reactive protein with anyother suitable drug. Moreover, one of ordinary skill in the art willunderstand that other parameters that track any marker in relation toany drug or pharmaceutical may be used without departing from the scopeof the present disclosure. The example PD model used herein is aturnover model. However, generally, any suitable PD model may be usedwithout departing from the scope of the present disclosure. The specifictype of PD model may depend on the drug and the PD marker. Examples oftypes of PD models are not limited to turnover models and include directeffect PD models, link effect PD models, indirect effect or turnover PDmodels, transit PD models, or any suitable combination thereof.

In the PD model 222 shown in FIG. 2 , a pharmacodynamics effectcompartment 216 is depicted as having an input Ksyn 214 and an outputKdeg 218. In the PD model 222, the overall effect of a drug on a bodymay be represented as in EQ. 13, where the values for Concentration,Emax, and EC50 are as described in relation to EQS. 7, 8, and 9,respectively.

$\begin{matrix}{{Effect} = \frac{E\max*{Concentration}}{{{EC}50} + {{Conc}entration}}} & {{EQ}.13}\end{matrix}$

Moreover, the PD effect compartment 216 represents an amount ofclearance of the biomarker that is mediated by the presence of the drug.In particular, EQ. 14 describes a differential equation for the flowrate of the biomarker or PD endpoint (e.g., a quantity that ismeasurable on a patient and is indicative of a PD response A(3)) withinthe PD effect compartment 216, and relates to the synthesis rate Ksyn,the degradation rate Kdeg, and the overall effect of the drug Effect tothe measured level of biomarker.

$\begin{matrix}{\frac{{dA}(3)}{dt} = {{Ksyn} - {{Kdeg}*{A(3)}*\left( {1 + {Effect}} \right)}}} & {{EQ}.14}\end{matrix}$

In particular, the concentration of the drug (from EQ. 7) (predicted bythe model) is used to drive the response of the PD model by firstproviding the individually estimated concentration into EQ. 13 and EQ14, which are updated simultaneously to determine the overall effect ofthe drug, and to determine a predicted time profile of the responseA(3). In general, the predicted concentration 212 may be provided to thePD model 222 rather than the measured concentration profile, at leastbecause the predicted concentration provides a smoother function to fitto the PD model 222. An initial condition for the PD compartment for theresponse A(3) may be set to the Base parameter, which represents the PDresponse measurement prior to the initiation of treatment.

Then, in a similar manner as described in relation to EQ. 7, the timeprofile of the predicted value of the PD marker A(3) is fit to thepatient's measured Response, as is shown in EQ. 15, where ε₂ representsthe residual error for the PD evaluation and corresponds to measurementerror.

Response=A(3)*exp(ε₂)  EQ. 15

The optimization may be performed to minimize the residual error betweenthe measurements and predictions. In particular, the patient's measuredResponse to the drug IFX may correspond to a laboratory measurement of Creactive protein or fecal calprotectin. Alternatively or additionally,the patient's measured Response may include a categorical observation,such as endoscopic remission, which may indicate one of various states,including progressive disease, stable disease, partial response, orcomplete response.

With at least some drugs, the response of the system (as modeled by thePD model) affects the amount of the drug that remains in the system oris flushed out (as modeled by the PK model). Thus, the PK model (asdescribed in relation to EQS. 1-7) may be combined with the PD model (asdescribed in relation to EQS. 8-15) to form a PK/PD model that describesthe interrelation between the two models. To combine the two models, theequations above may be modified to include the effect of thepharmacodynamics on the pharmacokinetics.

In one example, this is performed by updating the clearance 210 (fromthe PK model) to reflect the response A(3) (from the PD model), as isshown in EQ. 16.

CLT=CL+A(3)*exp(−K*t)  EQ. 16

The parameter CLT corresponds to a total clearance metric and isrepresented as an addition between the original PK clearance parameter(CL) and the PD estimate for the PD marker A(3), modified by a rateconstant K. In this example, the rate constant K reflects thediminishing effect of the PD response on CL over time. Even though thevalue for the amount A(3) is also generally expected to diminish withtime (due to the presence of the drug), the rate constant K represents aseparate effect, as the PD endpoint approaches normal ranges. Inparticular, as the patient heals and the disease is eliminated, thetotal clearance CLT approaches the PK clearance CL.

One example computational model for IFX is described herein forillustrative purposes, but in general, one of ordinary skill in the artwill understand that the systems and methods of the present disclosureare applicable to any computational model that involves bothpharmacokinetics and pharmacodynamics to estimate a target value for aphysiological parameter. In particular, a two-compartment model has beenshown and described in relation to FIG. 2 , but in general, any numberof compartments may be used without departing from the scope herein. Inparticular, the PK model may include a non-compartmental component, orbe a single-compartment, multi-compartmental, or any other suitable PKmodel. Moreover, depending on the particular disease, disease status,and the drug, different mathematical functions and equations thatdescribe the impact of the PD marker on the body may be used in themodels, without departing from the scope of the present disclosure.

Importantly, the above IFX example illustrates that not only do thesystems and methods of the present disclosure provide a recommendeddosing regimen to achieve a particular target level for a physiologicalparameter (e.g., concentration level of a drug or biomarker in blood),but the present disclosure also is able to evaluate whether theparticular target level would be effective for a specific patientexhibiting specific characteristics and responses. As described above,the target level of concentration EC50 (which is derived from theindividual) needed to achieve an effective response from the patient isestimated specifically for a particular patient. The estimate for EC50may be periodically updated, such as whenever any additional patientmeasurement data is recorded, or when a threshold amount of additionalpatient measurement data is received. For example, depending on thecomputational complexity of the models used, it may be undesirable tore-run the computational models whenever any measurement is taken fromthe patient. Instead, it may be desirable to wait until a full set ofmeasurements is taken, or until enough data is collected that deviatesfrom what is expected. As used in the above example, the targetconcentration level corresponds to a concentration level that wouldcause the patient to respond with a clinically meaningful effect. Ingeneral, the target concentration level may correspond to any suitablefraction of a maximal effect, without departing from the scope of thepresent disclosure.

FIG. 3 is a flowchart of a method 300 that may be implemented by thesystem 100 to provide a recommended treatment plan to a specificpatient, where the treatment plan is designed particularly for thespecific patient, based on the patient's measurements and data. Ingeneral, the method 300 provides an analysis of the patient's specificdata and determines an appropriate treatment plan or dosing regimensuitable for recommendation to the patient. An overview of the method300 will first be provided, followed by illustrations of variousimplementations of the steps of the method. As shown, the method 300generally includes the steps of receiving an input indicative of thepatient's measurements (step 302). The patient's measurements mayinclude a disease status of the patient and/or a history of thepatient's responses to dosing regimens of medications that have beenpreviously administered to the patient. Based on the patient'smeasurements, a therapeutic drug for administering to the patient isidentified (step 304). The method further includes identifying a modelfor predicting the patient's response to the drug (step 306), andproviding the patient's measurements to the model (step 308). Then, anoptimization technique on the model is run to generate a recommendeddosing regimen for the specific patient (step 310), and the recommendeddosing regimen is provided to a user interface (step 312). After therecommended dosing regimen (or a variant of the recommended dosingregimen) is administered to the patient, additional data may be recordedfrom the patient, and data indicative of the patient's response to theadministered dosing regimen is received (step 314). Based on thepatient's response data, it is determined whether to re-run the model(decision block 316). If so, the method 300 returns to step 308 toprovide the patient's updated measurements to the model, and if not, themethod 300 ends until additional patient response data is received towarrant a re-running of the model.

At step 302, an input indicative of patient measurements is received.For example, as was described in relation to FIG. 1 , the medicalprofessional 118 makes measurements from the patient 116. The medicalprofessional 118 may draw a sample of the blood of the patient 116, andmay measure a concentration of a biomarker in the blood sample. Ingeneral, the medical professional 118 may make any suitable measurementof the patient 116, including lab results such as concentrationmeasurements from the patient's blood, urine, saliva, or any otherliquid sampled from the patient. The measurement may includeobservations made by the medical professional 118 of the patient 116,including any symptoms exhibited by the patient 116. For example, themedical professional 118 may perform an examination of the patientgather or measure patient-specific factors such as sex, age, weight,race, disease stage, disease status, prior therapy, other concomitantdiseases and/or other demographic and/or laboratory test resultinformation.

At step 304, a therapeutic drug is identified for administering to thepatient. The medical professional 118 may already know which drug shouldbe administered to the patient 116, and so may provide the name or otheridentifier of the drug to the clinical portal 114. The drug may bedetermined based on an assessment of the patient's disease status.

At step 306, a model is identified for predicting the patient's responseto the drug identified at step 304. In particular, one or moreappropriate computational models may be selected from the modelsdatabase 106. As is described in the '545 application, a Bayesian modelaveraging approach may be optionally used to generate a composite modelto predict patient response. The averaging may be used when multiplepatient response models are available, and corresponding weights may beassigned to each patient response model, where the weights correspond toa level of confidence in each model. In an example, multiple PK and/orPD models are tested, and those models that have better performance(e.g., by fitting the measurement data better than other models) may bedetermined to be more likely than other models, and accordingly areassigned higher weights or ranks. As an example, there may be multiplePK models or multiple PD models, and the response of each model may beaveraged across the multiple models. As an example, a single PD modelmay include multiple paths, where each path describes a causalrelationship between the administration of the drug and its effect onthe patient's body. For example, one effect of the drug on the patient'sbody is that the patient may improve, while another possible effect isthat the patient may not improve or worsen. In this case, averaging mayoccur over the various paths of the model. In general, the “composite”model may refer to the averaged model when multiple patient responsemodels are available, to a single model, or to one of several possiblemodels describing different paths or trajectories.

At step 308, the patient measurements received at step 302 are providedas inputs into the model identified at step 306. In particular, as wasdescribed in the example shown in FIG. 2 , various parameters of thecomputational model may be set in accordance with the patientmeasurements. For example, the patient's weight, ALB, AST, and IRP maybe values that are readily measurable and input into EQS. 1-4. Moreover,the patient's concentration time profile may be measured and used as theparameter “concentration” in EQ. 7. The example described in relation toFIG. 2 is shown for illustrative purposes only, and one will understandthat any suitable measurements may be made and provided as input to acomputational model.

At step 310, an optimization technique is run on the model to generate arecommended dosing regimen. In particular, as was described in the '545application, a Bayesian forecasting process may be used to test variousdosing regimens for the patient 116 as a function of the patient'sspecific characteristics accounted for as patient factor covariateswithin the models, and the composite mathematical model. Thisforecasting involves evaluating dosing regimens based on predictedresponses for a typical patient with the patient-specificcharacteristics. Generally, Bayesian forecasting involves usingmathematical model parameters to forecast the likely response that aspecific patient will exhibit with various dosing regimens. Notably,forecasting allows for determination of a likely patient response to aproposed dosing regimen before actual administration of a proposeddosing regimen. Accordingly, the forecasting can be used to testmultiple different proposed dosing regimens (e.g., varying dose amount,dose interval and/or route of administration) to determine how eachdosing regimen would likely impact the patient, as predicted by thepatient-specific factors and/or data in the model/composite model.

More specifically, the server 104 performs multiple forecasts of patientresponses to evaluate multiple proposed dosing regimens based on thepatient's characteristics, by referencing and/or processing thecomposite model. Then, each dosing regimen is determined to be adequateor inadequate for meeting treatment objective or target profile. Forexample, the target profile may involve maintenance of a trough bloodconcentration level above a therapeutic threshold. Further, the server104 may compare forecasts of patient responses to various dosingregimens, and create a set of satisfactory or best dosing regimens forachieving the treatment objective or target profile. These satisfactoryor best dosing regimens may correspond to those dosing regimens that arerecommended for the patient 116.

At step 312, the one or more recommended dosing regimens are provided tothe user interface 112 in the clinical portal 114. The medicalprofessional 118 may then browse the recommended one or more dosingregimens before selecting a dosing regimen for administration to thepatient 116. In doing this, the medical professional 118 may select adosing regimen from the list, or may modify the recommended dosingregimen, in accordance with his/her judgment. Various considerations maybe taken into consideration by the medical professional 118 and/or theserver 104 in determining recommended dosing regimens. For example, aprimary consideration may be meeting a specific treatment objective,such as maintaining a minimum blood level concentration or maintaining atarget blood pressure, for example. However, other considerations mayalso be taken into consideration, such as ease of compliance, schedulingconsideration, or medication/treatment cost, for example. The system mayinclude utility functions for taking such other considerations intoaccount when determining the recommended dosing regimens. Then, themedical professional 118 directly or indirectly administers the dosingregimen (which may be the same or different from the recommended dosingregimen) to the patient, and later follows up with the patient 116 tocheck the patient's response to the dosing regimen.

In some implementations, the recommended dosing regimen is provided witha confidence interval that indicates a likelihood that the particulardosing regimen will be therapeutically effective for the patient 116. Inparticular, the confidence interval of the projected response orconcentration from the individual data may be assessed based on thecomplexity of the multiple computational models and the amount ofindividual data (PK and/or PD data). In particular, the confidenceinterval may reflect the possible error in the individual predictionsfrom the models. Initially, when no individual measurements have beentaken from the patient 116, the model's predictions have an errorassociated with them that is approximately equal to the unexplainedvariability in the PK and the PD models. However, as individualmeasurements are taken and introduced into these models, the error (orequivalently, the confidence interval) decreases before ultimatelyapproaching the assay error, which may correspond to a measurementerror. Moreover, the confidence intervals may be provided to theclinical portal 114, to give the medical professional 118 a sense forthe amount of error remaining in the model predictions.

At step 314, the patient's response data is received, and at decisionblock 316, it is determined whether to re-run or update the model. Inparticular, the medical professional 118 may determine that anadjustment to the dosing regimen is warranted if the patient's responseis deficient or not as expected. In this case, the medical professional118 may take additional measurements from the patient 116, and providethese additional measurements to the clinical portal 114. Alternatively,the medical professional 118 may provide the patient's response data tothe server 104, which determines whether the patient's response is asexpected or deficient, and subsequently determines whether to re-run themodel. If it is determined to not re-run the model, the method 300 endsor returns to step 314 to receive additional patient response data andre-evaluates whether to re-run the model at decision block 316.

If it is determined to re-run the model, the method 300 returns to step308 to provide the patient response data received at step 314 to themodel. In particular, as is described in the '545 application, aBayesian update process may be used to update the composite model basedon the patient's response to the dosing regimen. Each of the underlyingmathematical models are updated to reflect the patient's specificcharacteristics and response. Generally, Bayesian updating involves aBayesian inference, which is a method in which Bayes' rule is used toupdate the probability estimate for a hypothesis as additional evidenceis obtained. Bayesian updating is especially important in the dynamicanalysis of data collected over time (sequentially). The method asapplied here uses models that describe not only the time course ofexposure and/or response, but also include terms describing theunexplained (random) variability of exposure and response. The result ofBayesian updating is a set of parameters conditional to the observeddata. The process involves sampling parameters from a prior distribution(e.g., the underlying models) and calculating the expected responsesbased on the underlying models. For each underlying model, thedifference between the model expectation and the observed data iscompared. This difference is referred to as the “objective function.”The parameters are then adjusted based on the objective function, andthe new parameters are tested against the observed data by comparing thedifference between the new model expectation and the observed data. Thisprocess runs iteratively until the objective function is minimized,suggesting that the parameters that minimize the objective function bestdescribe the current data. All underlying models are thus subjected toBayesian updating. Once all models have been updated, Bayesian averagingmay be repeated to produce a new composite model. In someimplementations, a random function may be used to interject somevariation to ensure that a global minimum of the objective function hasbeen obtained.

FIGS. 4A, 4B, 5A, and 5B are example displays of the user interface 112on the clinical portal 114, according to an illustrative implementation.The display shown in FIG. 4A provides a screen that includes the IFXmodel predictions in accordance with the model described in relation toEQS. 1-16. In particular, the image on the right side of FIG. 4A depictsa predicted IFX concentration in a solid curve (as assessed by the PK/PDmodel) on the y-axis versus time on the x-axis. As is shown in FIG. 4A,a critical trough value (in μg/mL) is set by the user (or by default) toa value of 3 μg/mL. The critical trough value corresponds to a thresholdconcentration level, where it is undesirable for the patient'sconcentration to be below the critical trough value. The triangle inFIG. 4A indicates a dose of IFX that is administered to the patient(e.g., last administered on Nov. 1, 2014), and the graph indicates thatthe model predicts that the patient's concentration of IFX will hit thecritical trough value on Nov. 20, 2014, and the graph suggests thatanother administration of a dose of IFX may be administered on thatdate. The solid dot near Jan. 1, 2015 indicates the measured IFXconcentration, and represents the patient's measurement data to whichthe model predictions are fit.

In FIG. 4B, the user has provided an input dosing regimen for testing bythe model. In particular, the user has set the number of doses to three,the dose interval to 28 days, and the dose to 400 mg. In this case, thesystems and methods of the present disclosure provide a predictedconcentration profile for a specific patient based on the input dosingregimen (solid line), where the three doses are administered every 28days beginning Jan. 1, 2015, and are indicated by the triangles at thetop of the graph. The graph also includes markers at locations where thepredicted concentration profile (solid line) intersects the criticaltrough value (of 3 μg/mL). In particular, the feedback from the model inFIG. 4B is that the input dosing regimen provided by the user isinsufficient to keep the patient's predicted concentration levelentirely above the critical trough value between doses, but is veryclose to achieving this goal. In this case, the user may adjust theinput dosing regimen to lower the dose interval, increase the dose, or acombination thereof. In addition, the graph shown in FIG. 4B includes adashed line, which is representative of a typical patient'sconcentration time profile, and is not based on the patient's individualmeasurements.

FIG. 5A depicts an example display screen that displays predictedconcentration time profiles for four different dosing regimens. Inparticular, each dosing regimen has a corresponding dose interval (5weeks, 4 weeks, 3 weeks, and 2 weeks), where the dose amount decreasesas dose interval decreases (as is indicated by the height of the secondpeak in each predicted concentration time profile). In this case, theuser has selected to plot IFX concentration versus time, and to allowthe computational models to run to identify recommended dosing regimensto maintain IFX concentrations above the critical trough value.

In FIG. 5B, the user has selected to display the results from the plotshown in FIG. 5A in a table form. In particular, the example displayscreen in FIG. 5B lists the last dose date (Jan. 2, 2015), variousdosing intervals, a trough date (corresponding to the first date afterthe dose date that the predicted concentration time profile falls to orat the critical trough value), the suggested dose (in mg), thenormalized suggested dose (in mg/kg), the number of vials used for eachdose, and the target concentration (in ng/ml). As is shown in FIG. 5B, aset of proposed dosing schedules is shown, where the dosing scheduleshave different dose intervals ranging from two to eight weeks. Whilesome of the dosing schedules with longer dose intervals (six to eightweeks) are not recommended, four dosing schedules (with dose intervalsof two to five weeks) are proposed with doses that increase as doseinterval increases. In particular, when interacting with the displayscreen of FIG. 5B, the medical professional 118 may select a dosingregimen based on a specific goal. For example, the longer dose interval(e.g., five weeks) may be selected if it is desirable to administerdoses to the patient 116 infrequently. Alternatively, since patients areoften charged the price of a full vial, even when a partial vial isused, it may be desirable to use as much of the vials as possible. Inthis case, the four-week dosing regimen may be selected, since 4.9 vialsare used for each dose, and leads to little wastage of the drug (e.g.,only 0.1 vials per dosage). Alternatively, a shorter dose interval(e.g., two weeks) may be selected it if is desirable to administer dosesto the patient 116 more frequently, or to charge the patient 116 foronly two vials at a time.

FIG. 6 is a block diagram of a computing device, such as any of thecomponents of the systems of FIGS. 1A-1C, for performing any of theprocesses described herein. Each of the components of these systems maybe implemented on one or more computing devices 600. In certain aspects,a plurality of the components of these systems may be included withinone computing device 600. In certain implementations, a component and astorage device may be implemented across several computing devices 600.

The computing device 600 includes at least one communications interfaceunit, an input/output controller 610, system memory, and one or moredata storage devices. The system memory includes at least one randomaccess memory (RAM 602) and at least one read-only memory (ROM 604). Allof these elements are in communication with a central processing unit(CPU 606) to facilitate the operation of the computing device 600. Thecomputing device 600 may be configured in many different ways. Forexample, the computing device 600 may be a conventional standalonecomputer or alternatively, the functions of computing device 600 may bedistributed across multiple computer systems and architectures. In FIG.6 , the computing device 600 is linked, via network or local network, toother servers or systems.

The computing device 600 may be configured in a distributedarchitecture, wherein databases and processors are housed in separateunits or locations. Some units perform primary processing functions andcontain at a minimum a general controller or a processor and a systemmemory. In distributed architecture implementations, each of these unitsmay be attached via the communications interface unit 608 to acommunications hub or port (not shown) that serves as a primarycommunication link with other servers, client or user computers andother related devices. The communications hub or port may have minimalprocessing capability itself, serving primarily as a communicationsrouter. A variety of communications protocols may be part of the system,including, but not limited to: Ethernet, SAP, SAS™, ATP, BLUETOOTH™, GSMand TCP/IP.

The CPU 606 includes a processor, such as one or more conventionalmicroprocessors and one or more supplementary co-processors such as mathco-processors for offloading workload from the CPU 606. The CPU 606 isin communication with the communications interface unit 608 and theinput/output controller 610, through which the CPU 606 communicates withother devices such as other servers, user terminals, or devices. Thecommunications interface unit 608 and the input/output controller 610may include multiple communication channels for simultaneouscommunication with, for example, other processors, servers or clientterminals.

The CPU 606 is also in communication with the data storage device. Thedata storage device may include an appropriate combination of magnetic,optical or semiconductor memory, and may include, for example, RAM 602,ROM 604, flash drive, an optical disc such as a compact disc or a harddisk or drive. The CPU 606 and the data storage device each may be, forexample, located entirely within a single computer or other computingdevice; or connected to each other by a communication medium, such as aUSB port, serial port cable, a coaxial cable, an Ethernet cable, atelephone line, a radio frequency transceiver or other similar wirelessor wired medium or combination of the foregoing. For example, the CPU606 may be connected to the data storage device via the communicationsinterface unit 608. The CPU 606 may be configured to perform one or moreparticular processing functions.

The data storage device may store, for example, (i) an operating system612 for the computing device 600; (ii) one or more applications 614(e.g., computer program code or a computer program product) adapted todirect the CPU 606 in accordance with the systems and methods describedhere, and particularly in accordance with the processes described indetail with regard to the CPU 606; or (iii) database(s) 616 adapted tostore information that may be utilized to store information required bythe program.

The operating system 612 and applications 614 may be stored, forexample, in a compressed, an uncompiled and an encrypted format, and mayinclude computer program code. The instructions of the program may beread into a main memory of the processor from a computer-readable mediumother than the data storage device, such as from the ROM 604 or from theRAM 602. While execution of sequences of instructions in the programcauses the CPU 606 to perform the process steps described herein,hard-wired circuitry may be used in place of, or in combination with,software instructions for implementation of the processes of the presentinvention. Thus, the systems and methods described are not limited toany specific combination of hardware and software.

Suitable computer program code may be provided for performing one ormore functions described herein. The program also may include programelements such as an operating system 612, a database management systemand “device drivers” that allow the processor to interface with computerperipheral devices (e.g., a video display, a keyboard, a computer mouse,etc.) via the input/output controller 610.

The term “computer-readable medium” as used herein refers to anynon-transitory medium that provides or participates in providinginstructions to the processor of the computing device 600 (or any otherprocessor of a device described herein) for execution. Such a medium maytake many forms, including but not limited to, non-volatile media andvolatile media. Non-volatile media include, for example, optical,magnetic, or opto-magnetic disks, or integrated circuit memory, such asflash memory. Volatile media include dynamic random access memory(DRAM), which typically constitutes the main memory. Common forms ofcomputer-readable media include, for example, a floppy disk, a flexibledisk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM,DVD, any other optical medium, punch cards, paper tape, any otherphysical medium with patterns of holes, a RAM, a PROM, an EPROM orEEPROM (electronically erasable programmable read-only memory), aFLASH-EEPROM, any other memory chip or cartridge, or any othernon-transitory medium from which a computer can read.

Various forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to the CPU 606 (or anyother processor of a device described herein) for execution. Forexample, the instructions may initially be borne on a magnetic disk of aremote computer (not shown). The remote computer can load theinstructions into its dynamic memory and send the instructions over anEthernet connection, cable line, or even telephone line using a modem. Acommunications device local to a computing device 600 (e.g., a server)can receive the data on the respective communications line and place thedata on a system bus for the processor. The system bus carries the datato main memory, from which the processor retrieves and executes theinstructions. The instructions received by main memory may optionally bestored in memory either before or after execution by the processor. Inaddition, instructions may be received via a communication port aselectrical, electromagnetic or optical signals, which are exemplaryforms of wireless communications or data streams that carry varioustypes of information.

It is to be understood that while various illustrative implementationshave been described, the forgoing description is merely illustrative anddoes not limit the scope of the invention. While several examples havebeen provided in the present disclosure, it should be understood thatthe disclosed systems, components and methods of manufacture may beembodied in many other specific forms without departing from the scopeof the present disclosure.

The examples disclosed can be implemented in combinations orsub-combinations with one or more other features described herein. Avariety of apparatus, systems and methods may be implemented based onthe disclosure and still fall within the scope of the invention. Also,the various features described or illustrated above may be combined orintegrated in other systems or certain features may be omitted, or notimplemented.

While various implementations of the present disclosure have been shownand described herein, it will be obvious to those skilled in the artthat such implementations are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the disclosure. It should beunderstood that various alternatives to the implementations of thedisclosure described herein may be employed in practicing thedisclosure.

All references cited herein are incorporated by reference in theirentirety and made part of this application.

1. A system for determining a personalized dose of a pharmaceutical foran individual, the system comprising: an input port configured toreceive: first data representative of one or more characteristics of theindividual prior to administration of the pharmaceutical; second datarepresentative of a measurement of a physiological parameter of theindividual after administration of the pharmaceutical; a computerprocessor in communication with the input port and an electronicdatabase having information that represents a computational model topredict an effect of the pharmaceutical on the individual's body, thecomputational model including a pharmacokinetic component and apharmacodynamic component, and the computer processor being configuredto: generate, based on the first data and the computational model, afirst target concentration and one or more first doses determined tolikely achieve the first target concentration for the pharmaceutical inthe individual's body; compute, based on the second data, an update tothe pharmacokinetic component and the pharmacodynamic component of thecomputational model to obtain an updated computational model thatreflects the measurement of the physiological parameter; and generate,based on the updated computational model, a second target concentrationand one or more second doses determined to likely achieve the secondtarget concentration for the pharmaceutical in the individual's body,wherein the update to the pharmacodynamic component of the computationalmodel is used to predict that the second target concentration will havea therapeutic effect on the individual.
 2. The system of claim 1,wherein the pharmacokinetic component of the computational modelincludes a compartmental model, and the computer processor is configuredto use the pharmacokinetic component to predict a concentration timeprofile of the pharmaceutical in at least one compartment in thecompartmental model.
 3. The system of claim 2, wherein the predictedconcentration time profile is predicted by using a first differentialequation that describes a flow rate of the pharmaceutical into and outof the at least one compartment in the compartmental model.
 4. Thesystem of claim 1, wherein: the pharmacodynamic component of thecomputational model includes a synthesis rate parameter representativeof a synthesis rate of a pharmacodynamic marker and a degradation rateparameter representative of a degradation rate of the pharmacodynamicmarker; and the synthesis rate parameter and the degradation rateparameter are used in a second differential equation that predicts theindividual's response to the pharmaceutical.
 5. The system of claim 1,wherein the physiological parameter is a measured concentration timeprofile of the pharmaceutical in the individual's blood, tissue, orcells, and the computer processor generates the second targetconcentration and the one or more second doses by comparing the measuredconcentration time profile to the predicted concentration time profile.6. The system of claim 1, wherein the computer processor generates thesecond target concentration and the one or more second doses byperforming an optimization technique to minimize a difference betweenthe measured concentration time profile and the predicted concentration.7. The system of claim 1, wherein the pharmaceutical is infliximab, andthe pharmacodynamic component of the computational model reflects aneffect of infliximab on the individual's body.
 8. The system of claim 1,wherein the modified flow rate accounts for the individual's predictedresponse to the infliximab as the individual heals.
 9. The system ofclaim 1, wherein the first target concentration and the second targetconcentration each corresponds to a concentration that is predicted tocause an effect in the individual's body that is half of a predictedmaximal effect.
 10. The system of claim 1, wherein: the first targetconcentration and the one or more first doses are portions of a firstdosing regimen that includes recommended times and doses to administerto the individual; the input port is further configured to receive thirddata indicative of one or more requirements set by a manufacturer of thepharmaceutical; and the computer processor is further configured tomodify the first dosing regimen to comply with the one or morerequirements while simultaneously using the computational model toreduce an adverse effect of modifying the first dosing regimen.
 11. Amethod for determining a personalized dose of a pharmaceutical for anindividual, the method comprising: receiving, at an input port, firstdata representative of one or more characteristics of the individualprior to administration of the pharmaceutical; generate, at a computerprocessor, based on the first data and a computational model, a firsttarget concentration and one or more first doses determined to likelyachieve the first target concentration for the pharmaceutical in theindividual's body, wherein the computer processor is in communicationwith the input port and an electronic database having information thatrepresents the computational model to predict an effect of thepharmaceutical on the individual's body, the computational modelincluding a pharmacokinetic component and a pharmacodynamic component;receiving, at the input port, second data representative of ameasurement of a physiological parameter of the individual afteradministration of the pharmaceutical; computing, based on the seconddata, an update to the pharmacokinetic component and the pharmacodynamiccomponent of the computational model to obtain an updated computationalmodel that reflects the measurement of the physiological parameter; andgenerating, based on the updated computational model, a second targetconcentration and one or more second doses determined to likely achievethe second target concentration for the pharmaceutical in theindividual's body, wherein the update to the pharmacodynamic componentof the computational model is used to predict that the second targetconcentration will have a therapeutic effect on the individual.
 12. Themethod of claim 11, wherein the pharmacokinetic component of thecomputational model includes a compartmental model, and the methodfurther comprises using the pharmacokinetic component to predict aconcentration time profile of the pharmaceutical in at least onecompartment in the compartmental model.
 13. The method of claim 11,further comprising predicting the predicted concentration time profileby using a first differential equation that describes a flow rate of thepharmaceutical into and out of the at least one compartment in thecompartmental model.
 14. The method of claim 11, wherein: thepharmacodynamic component of the computational model includes asynthesis rate parameter representative of a synthesis rate of apharmacodynamic marker and a degradation rate parameter representativeof a degradation rate of the pharmacodynamic marker; and the synthesisrate parameter and the degradation rate parameter are used in a seconddifferential equation that predicts the individual's response to thepharmaceutical.
 15. The method of claim 11, wherein the physiologicalparameter is a measured concentration time profile of the pharmaceuticalin the individual's blood, tissue, or cells, and the second targetconcentration and the one or more second doses are generated bycomparing the measured concentration time profile to the predictedconcentration time profile.
 16. The method of claim 11, wherein thesecond target concentration and the one or more second doses aregenerated by performing an optimization technique to minimize adifference between the measured concentration time profile and thepredicted concentration.
 17. The method of claim 11, wherein thepharmaceutical is infliximab, and the pharmacodynamic component of thecomputational model reflects an effect of infliximab on the individual'sbody.
 18. The method of claim 11, wherein the modified flow rateaccounts for the individual's predicted response to the infliximab asthe individual heals.
 19. The method of claim 11, wherein the firsttarget concentration and the second target concentration eachcorresponds to a concentration that is predicted to cause an effect inthe individual's body that is half of a predicted maximal effect. 20.The method of claim 11, wherein the first target concentration and theone or more first doses are portions of a first dosing regimen thatincludes recommended times and doses to administer to the individual,and the method further comprises: receiving, at the input port, thirddata indicative of one or more requirements set by a manufacturer of thepharmaceutical; and modifying the first dosing regimen to comply withthe one or more requirements while simultaneously using thecomputational model to reduce an adverse effect of modifying the firstdosing regimen.